# What is soft margin in SVM?

What is soft margin in SVM?

## What is soft margin in SVM?

This idea is based on a simple premise: allow SVM to make a certain number of mistakes and keep margin as wide as possible so that other points can still be classified correctly. This can be done simply by modifying the objective of SVM.

## What is C in soft margin?

Soft margin SVM is implemented with the help of the Regularization parameter (C). Regularization parameter (C): It tells us how much misclassification we want to avoid. – Hard margin SVM generally has large values of C. – Soft margin SVM generally has small values of C.

What is soft margin in machine learning?

The constraint of maximizing the margin of the line that separates the classes must be relaxed. This is often called the soft margin classifier. This change allows some points in the training data to violate the separating line.

### How can we reduce overfitting in an SVM model?

In SVM, to avoid overfitting, we choose a Soft Margin, instead of a Hard one i.e. we let some data points enter our margin intentionally (but we still penalize it) so that our classifier don’t overfit on our training sample.

### What is a hard margin in SVM?

Solution: A A hard margin means that an SVM is very rigid in classification and tries to work extremely well in the training set, causing overfitting.

What is C in SVC?

C is the penalty parameter of the error term. It controls the trade off between smooth decision boundary and classifying the training points correctly. cs = [0.1, 1, 10, 100, 1000]for c in cs: svc = svm.SVC(kernel=’rbf’, C=c).fit(X, y)

#### What is C in linear SVC?

The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly.

#### What is soft margin and hard margin?

Soft Margin. The difference between a hard margin and a soft margin in SVMs lies in the separability of the data. If our data is linearly separable, we go for a hard margin. However, if this is not the case, it won’t be feasible to do that.

What is a hard margin?

## How do I know if SVM is overfitting?

You check for hints of overfitting by using a training set and a test set (or a training, validation and test set). As others have mentioned, you can either split the data into training and test sets, or use cross-fold validation to get a more accurate assessment of your classifier’s performance.

## Is SVM good for overfitting?

SVMs avoid overfitting by choosing a specific hyperplane among the many that can separate the data in the feature space. SVMs find the maximum margin hyperplane, the hyperplane that maximixes the minimum distance from the hyperplane to the closest training point (see Figure 2).

What is mean by hard margin?

A hard margin means that an SVM is very rigid in classification and tries to work extremely well in the training set, causing overfitting.